Matching-based Approach Research Articles

EFX allocations for indivisible chores: Matching-based approach

One of the most important topics in discrete fair division is whether an EFX allocation exists for any instance. Although the existence of EFX allocations is a standing open problem for both goods and chores, the understanding of the existence of EFX allocations for chores is less established compared to goods. We study the existence of EFX allocation for chores under the assumption that all agents' cost functions are additive. Specifically, we design polynomial time algorithms for computing EFX allocations for the following three cases: (i) the number of chores is at most twice the number of agents, (ii) the cost functions of all agents except for one induce the same ordering, and (iii) the number of agents is three and each agent has a personalized bi-valued cost function.

Power constraints and firm-level total factor productivity in developing countries

This paper analyzes the effects of power outages and constraints on manufacturing firms’ revenue-based total factor productivity in developing countries. The empirical analysis is based on the World Bank Enterprise Surveys dataset for 84 countries for the period 2006–2019. The paper starts by showing statistically that firms facing power outages differ and operate in very different environments compared to firms not facing power outages, underlining a potential nonrandom issue relating to the treatment variable. A matching-based approach is designed to contain this type of bias. It shows that power outages reduce firm-level revenue-based total factor productivity, with average productivity 11% lower for exposed firms compared to unexposed firms. Our results, robust to several tests, reveal some heterogeneity, particularly with regard to the level of development, country income group, and geographical location. In addition to the effects on productivity, the analyses highlight that power outages reduce business expansion, captured by sales growth, and employment. Moreover, we show that this effect is mitigated for firms with R&D activities and those equipped with backup power generators. Finally, transmission channel tests indicate that the negative effect of power outages on productivity is mainly driven by capacity utilization reduction and higher sales losses.

Efficient simultaneous optimization approach for the synthesis of large-scale heat-integrated water networks

This study develops a generalized superstructure and a compact mixed-integer nonlinear programming (MINLP) model for achieving efficient energy and water utilization in designing integrated process water systems with large sizes. Utilizing non-isothermal mixing reduces the need for many heat exchangers for indirect heat recovery. Hence, an exchanger-centric modeling strategy for heat integration is embraced over a hot–cold streams matching-based approach. A novel two-step approach is proposed to address the nonconvex and nonlinear challenges and ensure the efficiency of getting high-quality solutions. Initially, a targeting model incorporating linear heat integration is adopted to reduce the superstructure and partially initialize the design model. Subsequently, in the synthesizing stage, a two-level global search procedure is devised, integrating adaptive exchanger adjustment with multi-start local optimization. The robustness and efficiency of the proposed approach are confirmed through the resolution of four large-scale problems within one hour, yielding improved results compared to the best-known existing solutions.

MAB-RP: A Multi-Armed Bandit based workers selection scheme for accurate data collection in crowdsensing

Accurate data collection from workers is crucial for the success of Mobile Crowd Sensing (MCS) applications. However, Current studies exhibit several drawbacks. Firstly, the workers' sensing qualities remain unknown even after the platform acquires the data submitted by the workers, known as the Post Unknown Worker Selection (PUWS) problem. Secondly, systematic deviations between worker data and the Ground Truth Data (GTD) reduce the quality of MCS applications. Thirdly, the data collected by workers for different tasks may vary in accuracy, resulting in low-quality data collection. To address these challenges, we propose a novel Multi-armit-based worker selection scheme with reputation and preference (MAB-RP). The proposed scheme aims to select credible workers for high-quality data collection through trust identification, thus addressing the PUWS issue after worker recruitment. Additionally, the scheme employs a learning-based approach to identify and correct the gaps between the sensed data and the GTD, ultimately improving the accuracy of data collection. Lastly, a matching-based approach is used to identify workers' sensing qualities for different tasks, further enhancing the accuracy of data collection in MCS. Extensive simulations on real-world datasets demonstrate that the proposed MAB-RP scheme outperforms previous strategies in terms of both data quality and cost.

Impact of sarcosine on diabetic retinopathy: Findings based on weighted gene co-expression network analysis and machine learning techniques.

To quantify the association between serum sarcosine and diabetic retinopathy (DR) using weighted gene co-expression network analysis (WGCNA). We measured serum metabolites in 69 pairs of type 2 diabetes (T2D) patients with and without DR matched by age, gender, body mass index（BMI and HbA1c, using a propensity score matching-based approach. To identify modules and metabolites linked to DR, pathway analysis was performed using WGCNA, the Kyoto Encyclopedia of Genes and Genomes and Small-Molecule Pathway Database. The association of sarcosine with DR was estimated by restricted cubic spline and conditional logistic regression models. Its joint effects with covariates on DR were also extensively examined. With per interquartile range elevation of sarcosine, the adjusted odds ratio (AOR) of DR significantly decreased by 67% (AOR: 0.33, 95% confidence interval [CI]: 0.19-0.58). Similar results were also found in the tertile analysis. Compared with those in the first tertile of sarcosine, the AOR significantly decreased by 54% (AOR: 0.46, 95% CI: 0.18-1.17) and 78% (AOR: 0.22, 95% CI: 0.08-0.59) for subjects in the second and third tertiles, respectively. Compared with subjects with lower sarcosine and lower HDL-C levels, those with higher sarcosine and lower HDL-C levels had the lowest odds of DR (OR: 0.13, 95% CI: 0.04, 0.43). Serum sarcosine was inversely related to DR, especially in T2D patients with insufficient HDL-C. This study provides insights on a possible novel target for DR precision prevention and control, as well as a better understanding of the DR mechanism.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

Task Offloading in Wireless Powered Mobile Crowd Sensing: A Matching-Based Approach

Mobile crowd sensing (MCS) is a new sensing paradigm that leverages participatory sensing data from mobile devices for accomplishing large-scale sensing tasks. Incentivizing device owners to contribute high-quality sensing data is a prerequisite for the success of MCS services. In this paper, we first propose a pre-contracting incentive mechanism that involves the participation of not only the device owners located in close proximity to Point of Interests (PoIs) but also the device owners that are going to pass through those locations. Furthermore, the quality of sensing data is guaranteed through the use of redundancy. In particular, sensing data from multiple device owners is processed and compared at an edge side (i.e., base station) so as to detect the measurement error at the proximity of data sources. Simulation results confirm that the proposed incentive mechanism is efficient in terms of improving the total utility.

Potential conservation gains from improved protected area management in the Brazilian Amazon

Protected areas (PAs) are important policy instruments for forest conservation, but it is unclear if improved management can increase PA effectiveness. In Brazil, formal management plans are required to be in place shortly after the creation of a PA. This requirement is rarely enforced and, as a result, several PAs undergo many years without approved plans. We take advantage of this variation among PAs to study the impact of management plans on deforestation. We provide estimates from two quasi-experimental evaluation approaches based on the generalization of the difference-in-differences estimator: (1) matching-based methods for time-series cross-sectional data analysis and (2) the generalized synthetic control (GSC) method. We find weak, yet generally consistent, evidence across these two methods suggesting that PAs with approved management plans protect forests more effectively over time. Significant impact estimates from the matching-based approach ranged more widely than the GSC method (0.01%–0.09% versus 0.04%–0.05% of avoided deforestation per year, respectively). The effect size of these impacts is relatively substantial given that the average annual forest loss from our PA sample was 0.07% (±0.40%). To the extent that PAs with approved management plans reflect actual differences in PA management quality, our findings suggest that investments in improving PA management could result in positive conservation gains over time.

Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks

Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively.

GMatch: Knowledge base question answering via semantic matching

Effectiveness is essential for knowledge base question answering (KBQA) to determine whether the query can return the correct answers. Existing works for KBQA mainly focus on converting input questions into corresponding logic formats, such as SPARQL queries. However, since these works are largely decoupled from the knowledge base, the converted query may be ineffective. In this paper, we propose a novel semantic matching-based approach to model the query intention of the input question by extracting the subgraph of the knowledge base. The generation of the SPARQL query is reduced to semantic matching in the knowledge base to solve the ineffectiveness of the query. Firstly, a semantic query graph is proposed to model the reliable query intention of the input question. The SPARQL query graph could be extracted by matching the semantic query graph in the knowledge base. Secondly, an embedding-based method is developed to represent different forms of questions and queries in a common space. It is easy to detect semantic loss between the question and the converted query with the common representation. Finally, a data-driven semantic completion technique is presented to reduce the semantic loss by expanding the incomplete SPARQL query in the knowledge base. The experiments evaluated on benchmark datasets show that the proposed approach significantly outperforms state-of-the-art methods in efficiency and effectiveness. The code is available at https://github.com/offerhub-ht/gMatch.

Approximation algorithms for some min–max postmen cover problems

We investigate two min–max k-postmen cover problems. The first is the Min–Max Rural Postmen Cover Problem (RPC), in which we are given an undirected weighted graph and the objective is to find at most k closed walks, covering a required subset of edges, to minimize the weight of the maximum weight closed walk. The other is called the Min–Max Chinese Postmen Cover Problem, in which the goal is to find at most k closed walks, covering all the edges of an undirected weighted graph, to minimize the weight of the maximum weight closed walk. For both problems we propose the first constant-factor approximation algorithms with ratios 10 and 4, respectively. For the Metric RPC, a special case of the RPC with the edge weights obeying the triangle inequality, we obtain an improved 6-approximation algorithm by a matching-based approach. For the Min–Max Rural Postmen Walk Cover Problem (RPWC), a variant of the RPC with the closed walks replaced by (open) walks, we give a 5-approximation algorithm that improves on the previous 7-approximation algorithm. If k is fixed, we devise improved approximation algorithms for the Metric RPC and the RPWC with ratios $$4+\epsilon $$ and $$3+\epsilon $$ , respectively, where $$\epsilon >0$$ is an arbitrary small constant. The latter result improves on the existing $$(4+\epsilon )$$ -approximation algorithm. Moreover, we develop a $$(3+\epsilon )$$ -approximation algorithm for a special case of the RPC with fixed k, improving on the previous $$(4+\epsilon )$$ -approximation algorithm.

A Demand-Supply Matching-Based Approach for Mapping Renewable Resources Towards 100% Renewable Grids in 2050

Recently, many renewable energy (RE) initiatives around the world are based on general frameworks that accommodate the regional assessment taking into account the mismatch of supply and demand with pre-set goals to reduce energy costs and harmful emissions. Hence, relying entirely on individual assessment and RE deployment scenarios may not be effective. Instead, developing a multi-faceted RE assessment framework is vital to achieving these goals. In this study, a regional RE assessment approach is presented taking into account the mismatch of supply and demand with an emphasis on Photovoltaic (PV) and wind turbine systems. The study incorporates mapping of renewable resources optimized capacities for different configurations of PV and wind systems for multiple sites via test case. This approach not only optimizes system size but also provides the appropriate size at which the maximum renewable energy fraction in the regional power generation mix is maximized while reducing energy costs using MATLAB’s ParetoSearch algorithm. The performance of the proposed approach is tested in a realistic test site, and the results demonstrate the potential for maximizing the RE share compared to the achievable previously reported fractions. The results indicate the importance of resource mapping based on energy-demand matching rather than a quantitative assessment of anchorage sites. In the examined case study, the new assessment approach led to the identification of the best location for installing a hybrid PV / wind system with a storage system capable of achieving a nearly 100% autonomous RE system with Levelized cost of electricity of 0.05 USD/kWh.

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID.

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

Delay-Minimized Edge Caching in Heterogeneous Vehicular Networks: A Matching-Based Approach

To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.

Licensed and Unlicensed Bands Allocation for Cellular Users: A Matching-Based Approach

Long term evolution-unlicensed (LTE-U) technologies allow cellular users (CUs) to be served in unlicensed bands. However, how a CU properly chooses between licensed and unlicensed bands and how a CU can utilize the unlicensed bands without jeopardizing other unlicensed-band users, e.g., Wi-Fi users (WUs), remain as open problems. In this letter, we aim to optimize the allocation of radio resources in both licensed and unlicensed bands in an LTE-U and Wi-Fi coexisting scenario, where a CU can be allocated either a licensed or unlicensed band. We formulate a multi-objective optimization problem to maximize every CU's utility, while guaranteeing the throughput requirements of both CUs and WUs. We propose an algorithm based on one-to-one and many-to-one matching algorithms to solve it. The stability, Pareto optimality, and convergence of the proposed matching-based algorithm are proven. Simulation results show that the proposed algorithm can achieve near-optimal performance but with a low computational complexity.

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree.

Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.

Numerically Aware Orderings for Sparse Symmetric Indefinite Linear Systems

Sparse symmetric indefinite problems arise in a large number of important application areas; they are often solved through the use of an LDL T factorization via a sparse direct solver. While for many problems prescaling the system matrix A is sufficient to maintain stability of the factorization, for a small but important fraction of problems numerical pivoting is required. Pivoting often incurs a significant overhead, and consequently, a number of techniques have been proposed to try and limit the need for pivoting. In particular, numerically aware ordering algorithms may be used, that is, orderings that depend not only on the sparsity pattern of A but also on the values of its (scaled) entries. Current approaches identify large entries of A and symmetrically permute them onto the subdiagonal, where they can be used as part of a 2 × 2 pivot. This is numerically effective, but the fill in the factor L and hence the runtime of the factorization and subsequent triangular solves may be significantly increased over a standard ordering if no pivoting is required. We present a new algorithm that combines a matching-based approach with a numerically aware nested dissection ordering. Numerical comparisons with current approaches for some tough symmetric indefinite problems are given.

Moving Destination Prediction Using Sparse Dataset

Moving destination prediction offers an important category of location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to destination prediction is to match the given query trajectory with massive recorded trajectories by similarity calculation. Unfortunately, due to privacy concerns, budget constraints, and many other factors, in most circumstances, we can only obtain a sparse trajectory dataset. In sparse dataset, the available moving trajectories are far from enough to cover all possible query trajectories; thus the predictability of the matching-based approach will decrease remarkably. Toward destination prediction with sparse dataset, instead of searching similar trajectories over the sparse records, we alternatively examine the changes of distances from sampling locations to final destination on query trajectory. The underlying idea is intuitive: It is directly motivated by travel purpose, people always get closer to the final destination during the movement. By borrowing the conception of gradient descent in optimization theory, we propose a novel moving destination prediction approach, namely MGDPre. Building upon the mobility gradient descent, MGDPre only investigates the behavior characteristics of query trajectory itself without matching historical trajectories, and thus is applicable for sparse dataset. We evaluate our approach based on extensive experiments, using GPS trajectories generated by a sample of taxis over a 10-day period in Shenzhen city, China. The results demonstrate that the effectiveness, efficiency, and scalability of our approach outperform state-of-the-art baseline methods.

Real-time illumination and shadow invariant lane detection on mobile platform

In this work, a novel lane detection method using a single input image is presented. The proposed method adopts a color and shadow invariant preprocessing stage including a feature region detection method called as maximally stable extremal regions. Next, candidate lane regions are examined according to their structural properties such as width–height ratio and orientation. This stage is followed by a template matching-based approach to decide final candidates for lane markings. At the final stage of the proposed method, outliers are eliminated using the random sample consensus approach. The proposed method is computationally lightweight, and thus, it is possible to execute it in real-time on consumer-grade mobile devices. Experimental results show that the proposed method is able to provide shadow, illumination and road defects invariant performance compared to the existing methods.